“AI misdiagnosis” doesn’t usually mean a machine made a decision alone. More often, it involves automation-assisted steps that influence what clinicians see, what gets flagged, and what prompts (or fails to prompt) escalation.
In the Thornton area, common scenarios we see include:
- Imaging and radiology workflow issues: abnormal findings that should have been acted on promptly, missed in review, or inconsistently documented across reports.
- Triage and routing delays: patients directed to lower-acuity pathways even though symptoms suggested the need for additional testing.
- Lab result recognition problems: abnormal values not escalated, not communicated clearly, or not followed by timely return instructions.
- Follow-up breakdowns: the correct next step isn’t ordered or is missed after discharge—especially when families are coordinating care around work schedules.
Even when clinicians were involved, the legal question is whether the care met the standard of care under the circumstances—and whether deviations contributed to harm.


